Hybrid- and Multi-Cloud Architectures

Benefit from the independence, flexibility, and scalability of this modern architecture while ensuring transparency, governance and security.

Hybrid- and Multi-Cloud Architectures with the Data Virtuality Platform

Cloud platforms became very popular in recent years and are now an integral part for most enterprises. Some enterprises fully rely on solely one cloud platform infrastructure and build their architecture around it. But the dependence on a single cloud platform comes with several risks – especially the dependency aspect is a significant one. Therefore, the majority of companies are rather moving to a hybrid- or multi-cloud architecture approach in order to benefit from the independence, flexibility, security, and potential cost optimization that these modern architectures offer. The cloud data ecosystem plays an essential role in fully enabling multi- or hybrid-cloud architecture:

  1. To build the architecture that fits your specific needs, not the one that the cloud platforms of your choice impose on you.
  2. To fill the gaps that cannot be filled solely by one single cloud provider such as data integration, metadata management, data governance, and data quality.
  3. To avoid another silo built in the infrastructure of the cloud platform.
 

The Data Virtuality Platform helps to plug in the gaps of the cloud platforms with the different data management capabilities such as data integration, data quality, data governance, master data management, and metadata management so that you can build the architecture that you want!

Your Key Benefits with Data Virtuality

Fully Integrated Data Landscape

All data sources, whether in private or public cloud or on-premises can easily be integrated with the Data Virtuality Platform providing a single layer for data access and delivery. This way, data silos can be eliminated or avoided.

Easy Data Movements

The Data Virtuality Platform facilitates lifting data into the cloud and the exchange of data between different storage systems. The intelligent connectors support complex processes and optimize extract and load jobs of the data - even for large amounts of data. Ultimately, the data from various data sources is available in the different BI tools.

Transparency and Traceability

The data lineage features such as automatic data lineage, column-level data lineage, and persuasive audit trails in the Data Virtuality Platform give you full control over your data. With just one click, you can check where the data comes from, how it was manipulated, and by whom.

Compliant to Data Protection

With a fine-granular permission layer (schema, table, column-, and row-level), built-in user/role-based permission system, flexible data masking, versioning for all custom metadata, and other features in the Data Virtuality Platform, you can ensure a high level of security in your data landscape. Sensitive data can be securely managed and efficiency can be increased as the users can solely focus on the relevant data as the irrelevant data is not accessible. You can always see who had access to which data or who was modifying the data models. It empowers you to fully utilize your data while complying to data protection laws.

Related Resource

Best Practices for Hybrid- and Multi-Cloud Architectures

This whitepaper describes best practices for orchestrating Hybrid- and Multi-Cloud architectures and how Data Virtuality can enable these.

Data Fabric Q&A Paper with Forrester

In this Q&A paper Noel Yuhanna, VP and Principal Analyst at Forrester, answers frequently asked questions about the Data Fabric architecture.

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